code: def test_discrete_ppo_head():
      head = DiscretePPOHead(num_actions=2)
      head.initialize()
      middleware_data = mx.nd.random.uniform(shape=(10, 100))
      probs = head(middleware_data)
      assert probs.ndim == 2  # (batch_size, num_actions)
      assert probs.shape[0] == 10  # since batch_size is 10
      assert probs.shape[1] == 2
  
imports: import mxnet
  import numpy
  import os
  import pytest
  from scipy import stats
  import sys
  from rl_coach.architectures.head_parameters import PPOHeadParameters
  from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist, DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 18 for calling a global function with example usage: test_discrete_ppo_head() without return types